Talk by Garrett Kenyon to the Redwood Center for Theoretical Neuroscience at UC Berkeley.  Garrett is a Principle Investigator at Los Alamos National Laboratory in New Mexico.

Abstract:  The Locally Competitive Algorithm (LCA) is a neurally-plausible sparse solver based on lateral inhibition between leaky integrator neurons.  LCA accounts for many linear and nonlinear response properties of V1 simple cells, including end-stopping and contrast-invariant orientation tuning.  Here, we describe a convolutional implementation of LCA in which a column of feature vectors is replicated with a stride that is much smaller than the diameter of the corresponding kernels, allowing the construction of dictionaries that are many times more overcomplete than without replication. Using a local Hebbian rule that minimizes sparse reconstruction error,  we are able to learn representations from unlabeled imagery, including monocular and stereo video streams, that in some cases support near state-of-the-art performance on object detection, action classification and depth estimation tasks, with a simple linear classifier. We further describe a scalable approach to building a hierarchy of convolutional LCA layers, which we call a Deconvolutional Competitive Algorithm (DCA).  All layers in a DCA are trained simultaneously and all layers contribute to a single image reconstruction, with each layer deconvolving its representation through all lower layers back to the image plane. We show that a 3-layer DCA trained on short video clips obtained from hand-held cameras exhibits a clear segregation of image content, with features in the top layer reconstructing large-scale structures while features in the middle and bottom layers reconstruct progressively finer details. Lastly, we describe PetaVision, an open source, cloud-friendly, high-performance neural simulation toolbox that was used to perform the numerical studies presented here.

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